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workflow graph 16S metagenomic paired-end QIIME2 Sample (preprocessing)

A workflow for processing a single 16S sample via a QIIME2 pipeline. ## __Outputs__ #### Output files: - overview.md, list of inputs - demux.qzv, summary visualizations of imported data - alpha-rarefaction.qzv, plot of OTU rarefaction - taxa-bar-plots.qzv, relative frequency of taxomonies barplot ## __Inputs__ #### General Info - Sample short name/Alias: Used for samplename in downstream analyses. Ensure this is the same name used in the metadata samplesheet. - Environment: where the sample was collected - Catalog No.: catalog number if available (optional) - Read 1 FASTQ file: Read 1 FASTQ file from a paired-end sequencing run. - Read 2 FASTQ file: Read 2 FASTQ file that pairs with the input R1 file. - Trim 5' of R1: Recommended if adapters are still on the input sequences. Trims the first J bases from the 5' end of each forward read. - Trim 5' of R2: Recommended if adapters are still on the input sequences. Trims the first K bases from the 5' end of each reverse read. - Truncate 3' of R1: Recommended if quality drops off along the length of the read. Clips the forward read starting M bases from the 5' end (before trimming). - Truncate 3' of R2: Recommended if quality drops off along the length of the read. Clips the reverse read starting N bases from the 5' end (before trimming). - Threads: Number of threads to use for steps that support multithreading. ### __Data Analysis Steps__ 1. Generate FASTX quality statistics for visualization of unmapped, raw FASTQ reads. 2. Import the data, make a qiime artifact (demux.qza), and summary visualization 3. Denoising will detect and correct (where possible) Illumina amplicon sequence data. This process will additionally filter any phiX reads (commonly present in marker gene Illumina sequence data) that are identified in the sequencing data, and will filter chimeric sequences. 4. Generate a phylogenetic tree for diversity analyses and rarefaction processing and plotting. 5. Taxonomy classification of amplicons. Performed using a Naive Bayes classifier trained on the Greengenes2 database \"gg_2022_10_backbone_full_length.nb.qza\". ### __References__ 1. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, and Caporaso JG. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37: 852–857. https://doi.org/10.1038/s41587-019-0209-9

https://github.com/datirium/workflows.git

Path: workflows/qiime2-sample-pe.cwl

Branch/Commit ID: master
workflow graph SetParameterFromExternal

Receive parameter update (e.g., by querying an external source like a configuration or calibration database) or by expert input (e.g., by a member of a telescope team or a simulation pipeline expert).

 https://gitlab.desy.de/gernot.maier/cwlsandbox.git

Path: workflows/SetParameterFromExternal.cwl

Branch/Commit ID: main
workflow graph scatter-valuefrom-wf4.cwl#main

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/scatter-valuefrom-wf4.cwl

Branch/Commit ID: main

Packed ID: main
workflow graph scatter-wf4.cwl#main

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/scatter-wf4.cwl

Branch/Commit ID: bf93dd3e6e3261e1455530984ce045f283535d17

Packed ID: main
workflow graph create_snap_and_analyze.cwl

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: create_snap_and_analyze.cwl

Branch/Commit ID: v1.0
workflow graph grep-and-count.cwl

https://github.com/yonesora56/togotv_cwl_for_remote_container.git

Path: zatsu_cwl/grep-and-count.cwl

Branch/Commit ID: master
workflow graph hello_world.cwl

https://github.com/dockstore/hello_world.git

Path: hello_world.cwl

Branch/Commit ID: v1.0.0
workflow graph vcf_concat.cwl

https://github.com/andurill/ACCESS-Pipeline.git

Path: workflows/subworkflows/vcf_concat.cwl

Branch/Commit ID: master
workflow graph ValidationWorkflowMissing

This is a placeholder for a missing acceptance workflow.

 https://github.com/gammasim/workflows.git

Path: workflows/ValidationWorkflowMissing.cwl

Branch/Commit ID: main
workflow graph exomeseq-gatk4-preprocessing/v2.2.0

Whole Exome Sequence preprocessing using GATK4 - v2.2.0

 https://github.com/bespin-workflows/exomeseq-gatk4.git

Path: exomeseq-gatk4-preprocessing.cwl

Branch/Commit ID: develop